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Revista de Ciência e Engenharia Têxtil

Volume 2, Emitir 5 (2012)

Artigo de Pesquisa

Improving Properties of Polyester and Cellulose Acetate Fabrics using Laser Irradiation

Kamel MM, Raslan WM, Helmy HM and Al-Ashkar E

Polyester (PET) and Cellulose Acetate (CA) fabrics were modified by excimer laser irradiation. The induced surface changes were characterized by Scaning Electron Microscopy (SEM). The effect of laser treatment conditions such as power and time of irradiation on the dyeing behaviour of both polyester and cellulose acetate fabrics was studied. A greater depth of shade was achieved on laser treated fabrics compared to the untreated fabric dyed with disperse dye upon the same dyeing conditions. Fabric  wettability, surface roughness and fastness properties were also given. Microscopic analysis of the cross section of the dyed samples showed deeper penetration of dye molecules inside the interior structure of cellulose acetate fabric. The depth of disperse dye into CA fibers increased from 20.2% for untreated sample to 43.9% for treated one that may help to overcome the ring dyeing problem. Also, laser treatment of cellulose acetate was more effective in enhancing its dyeability with disperse dye than ultrasonic dyeing technique under the same dyeing conditions.

Artigo de Pesquisa

A Study on Effect of Strain Rate on Tensile Behavior of Inherent Flame Retardant Trevira CS Airjet Spun Yarns

Bharani M and Mahendra Gowda RVM

It is well known that the tensile behaviour of a spun yarn depends largely on the caharacteristics and structural arrangements of its constituent fibres. An air-jet spun yarn consisting of a core of parallel fibres wrapped by sheath fibres, exhibits a fasciated yarn structure. Therefore, due to the marked structural differences, the responses to the tensile forces of these yarns are expected to be different. Further, the theoretical analysis of the tensile behaviour of a staple-fibre spun yarn is highly complex, mainly because of discontinuities at fibre ends. For instance, during the insertion of weft, whether by projectile or air jet, the yarn has to withstand accelerations of many thousands of times greater than that due to gravity. Hence it becomes important to understand the stress-strain responses of yarns under non-standard loading conditions vary over a range of strain rates. Hence the present paper is designed to understand the how the (Trevira CS) inherently flame retardant fibres were successfully processed on air-jet spinning system to produce 20 Ne & 30 Ne yarns. As regards the influence of high strain rate on yarn tensile characteristics, it is found that an increase in strain rate from 5 m/min to 400 m/min considerably increases the yarn tenacity but decreases the yarn breaking extension. The reduction in tenacity is significant at the 95% level of confidence in case 30 Ne pure Trevira-spun and its blended yarns. This paper is written to understand the effect of strain rate on the tensile behaviour of Trevira CS fibre.

Artigo de Pesquisa

Treatment of Textile Wastewater by Nanofiltration Membranes: A Neural Network Approach

Jahangiri M and Aminian A

Textile industries represent an important environmental problem due to their high water consumption. In order to economically water consumption, wastewater treatment is necessary for water reuse in the textile industries. Predicting the performance of nanofiltration membrane, as an effective separation process, is necessary for the design and depiction of process. Prediction of the rejection of degradable components is especially important. In this work, an Artificial Neural Network (ANN) is used to predict the rejection of Chemical Oxygen Demand (COD) in a cross-flow nanofiltration membrane at textile wastewater effluent stream. Rejections are predicted as a function of feed pressure and permeate flux with cross flow velocity. ANN predictions of the COD rejection are compared with experimental results obtained using two different nanofiltration membranes (NF-90 and DK-5). The results show a good agreement between experimental data and the output from the neural network simulation.

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